{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOr6gE8NieRIaS7nhNc1Qc2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SJinLee/PPG/blob/main/04.Yurim_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "4rInmP6i_OmU"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/SJinLee/cryptology/main/YURIM_8min.txt',header=None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "U_Ru8mCVAUWM",
        "outputId": "64eb9385-2e08-423c-e822-c216c46bc480"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lowpass(ppg):\n",
        "    ppg2 = ppg.copy()\n",
        "    d = 0.92\n",
        "    a = d\n",
        "    b = 1-d\n",
        "    xi = ppg2['Red_Signal'].values[1:].astype(float).copy()\n",
        "    v = ppg2['Red_Signal'].values.astype(float).copy()\n",
        "    y = v[0]\n",
        "    i = 1\n",
        "    for x in xi:\n",
        "        y += b*(x-y)\n",
        "        v[i] = y\n",
        "        i += 1\n",
        "    ppg2['Red_Signal2'] = v\n",
        "    ppg2['Time'] = pd.to_datetime(ppg2['Time'])\n",
        "    return ppg2"
      ],
      "metadata": {
        "id": "a6iIylxhDCw5"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data2 = data.rename(columns={0:'Red_Signal'})\n",
        "data2['Time'] = pd.to_datetime('2024-04-11 00:00:00')+pd.Timedelta(milliseconds=1000/790)*data.index\n",
        "data3 = get_lowpass(data2)\n",
        "diff=data3['Red_Signal2'].diff()\n",
        "data3['peak'] = np.concatenate([[False],(diff.iloc[1:-1].values>0) & (diff.iloc[2:].values<0),[False]])\n",
        "data3['peak2'] = data3['peak'] & (data3['Red_Signal']>515)"
      ],
      "metadata": {
        "id": "4s1nv3wrDD5m"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h9n-ZyemyNtW",
        "outputId": "a51372e5-b260-4274-975f-990d89644d3d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Red_Signal                          Time  Red_Signal2   peak  peak2\n",
              "0                0 2024-04-11 00:00:00.000000000     0.000000  False  False\n",
              "1              500 2024-04-11 00:00:00.001265822    40.000000  False  False\n",
              "2              500 2024-04-11 00:00:00.002531644    76.800000  False  False\n",
              "3              500 2024-04-11 00:00:00.003797466   110.656000  False  False\n",
              "4              500 2024-04-11 00:00:00.005063288   141.803520  False  False\n",
              "...            ...                           ...          ...    ...    ...\n",
              "381202         506 2024-04-11 00:08:02.533878044   507.397729  False  False\n",
              "381203         506 2024-04-11 00:08:02.535143866   507.285910  False  False\n",
              "381204         506 2024-04-11 00:08:02.536409688   507.183037  False  False\n",
              "381205         506 2024-04-11 00:08:02.537675510   507.088394  False  False\n",
              "381206         506 2024-04-11 00:08:02.538941332   507.001323  False  False\n",
              "\n",
              "[381207 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5ed987c-c767-4ae5-80ac-0c09a2368f20\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Red_Signal</th>\n",
              "      <th>Time</th>\n",
              "      <th>Red_Signal2</th>\n",
              "      <th>peak</th>\n",
              "      <th>peak2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2024-04-11 00:00:00.000000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>500</td>\n",
              "      <td>2024-04-11 00:00:00.001265822</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>500</td>\n",
              "      <td>2024-04-11 00:00:00.002531644</td>\n",
              "      <td>76.800000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>500</td>\n",
              "      <td>2024-04-11 00:00:00.003797466</td>\n",
              "      <td>110.656000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>500</td>\n",
              "      <td>2024-04-11 00:00:00.005063288</td>\n",
              "      <td>141.803520</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381202</th>\n",
              "      <td>506</td>\n",
              "      <td>2024-04-11 00:08:02.533878044</td>\n",
              "      <td>507.397729</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381203</th>\n",
              "      <td>506</td>\n",
              "      <td>2024-04-11 00:08:02.535143866</td>\n",
              "      <td>507.285910</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381204</th>\n",
              "      <td>506</td>\n",
              "      <td>2024-04-11 00:08:02.536409688</td>\n",
              "      <td>507.183037</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381205</th>\n",
              "      <td>506</td>\n",
              "      <td>2024-04-11 00:08:02.537675510</td>\n",
              "      <td>507.088394</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381206</th>\n",
              "      <td>506</td>\n",
              "      <td>2024-04-11 00:08:02.538941332</td>\n",
              "      <td>507.001323</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>381207 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5ed987c-c767-4ae5-80ac-0c09a2368f20')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c5ed987c-c767-4ae5-80ac-0c09a2368f20 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c5ed987c-c767-4ae5-80ac-0c09a2368f20');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-66d30fd3-cf68-45a8-a0d5-cda844be912c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66d30fd3-cf68-45a8-a0d5-cda844be912c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-66d30fd3-cf68-45a8-a0d5-cda844be912c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data3"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def draw_ppg(ppg2,start,period):\n",
        "    t = ppg2['Time'][start:start+period]\n",
        "    x = ppg2['Red_Signal'][start:start+period]\n",
        "    y = ppg2['Red_Signal2'][start:start+period]\n",
        "    plt.plot(t,x,label='input')\n",
        "    plt.plot(t,y,label='output')\n",
        "    if 'peak2' in ppg2:\n",
        "        peak = ppg2['peak2'][start:start+period]\n",
        "        plt.plot(t.loc[peak],y.loc[peak],'og')\n",
        "    plt.legend()"
      ],
      "metadata": {
        "id": "NULqbLWbzdl8"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_ppg(data3,300,5000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "6UKCiZRvyVWT",
        "outputId": "b138e89f-f5f7-438b-9e33-51ba1c52caa5"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F3ZtIjZi12R3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}